1. Field of the Invention
The present invention relates to a control device in a vehicle and a method for calculating an output variable for a control system of functions of the vehicle.
2. Description of the Related Art
A variety of methods can be used in order to determine variables in vehicle control devices that cannot be measured or can be measured only with great difficulty (and thus too expensively for implementation in production vehicles), for example critical variables of the combustion process in an engine control device such as exhaust gas temperature, fill state, raw emissions values, efficiencies, consumption, etc., but that are needed by the control device in order to perform its control functions. One widely used method is that of characteristic curves, which can represent a one-dimensional correlation; or that of characteristics diagrams, which can represent a two- or multi-dimensional correlation. These characteristics diagrams can be defined/stored via interpolation points, and a prediction of the target variable for specific input values can be interpolated from the adjacent interpolation points, e.g. linearly or using splines (see, for example, published German patent application document DE 199 63 213 A1). Other methods are based on, as a rule, highly simplified physical models (see, for example, published German patent application document DE 10 2008 004 362 A1), which are often also represented by characteristics diagrams. Consideration is also given to data-based parametric regression models such as, for example, neural networks (e.g. published German patent application document DE 10 2007 008 514).
In the automotive sector, so-called Bayesian regressions are used not “online” (i.e. during normal operation of the vehicle), but instead “offline,” for example in a calibration phase of an engine; see, for example, “Bayesian statistics in engine mapping,” Ward, M. C., Brace, C. J., Vaughan, N. D., Shaddick, G., Ceen, R., Hale, T., Kennedy, G. in International Conference on Statistics and Analytical Methods in Automotive Engineering, IMechE Conference Transactions, pp. 3-15, 2002; and “Validation of Neural Networks in Automotive Engine Calibration,” D. Lowe and K. Zapart, Proceedings Conference on Artificial Neural Networks, 1997.
When characteristics diagrams are used to characterize the correlations, there is often a high degree of application complexity or low prognosis accuracy for multi-dimensional correlations. Creation of a reliable physical model results in a large development outlay, and it is not always possible to develop a physical model that is not too highly simplified, in particular given the complex events of the combustion process, which must embrace not only thermodynamics but also, for example, chemistry and flow mechanics. For the known methods, it is the case that they make no statements as to the expected accuracy. This can be important, however, in particular for critical target variables, in order to ensure a reliable open- or closed-loop control strategy.